does participation of women on micro and...

G.J.C.M.P.,Vol.3(2):109-119 (March–April,2014) ISSN: 2319 – 7285

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DOES PARTICIPATION OF WOMEN ON MICRO AND SMALL SCALE ENTERPRISES ADDRESS POVERTY IN NORTHERN ETHIOPIA? EVIDENCES FROM CENTRAL AND

NORTH-WEST ZONES OF TIGRAI ARAYA MEBRAHTU TEKA

LECTURER, DEPARTMENT OF ECONOMICS, ADIGRAT UNIVERSITY

P.O. BOX 161, ADIGRAT, TIGRAI, ETHIOPIA

Email:[email protected]

Abstract As an engine of economic growth and women empowerment, micro and Small Scale Enterprises (MSEs) have taken

considerable focus in Ethiopia. To explore the impact of women’s participation on MSEs on Poverty; primary data from

300 women MSE operators and non participants from Shire, Aksum and Adwa were collected. FGT, Gini index, logit

model and PSM were used to analyze the data. 24.2 percent of the households were living below the poverty line in

which 20.1 percent of the MSE participants and 27.2 percent of the MSE non participants are poor. Experiencing cyclical

moves in the growth rate of income, the mean monthly income of the participants was 2.165 times higher than that of non

participants and Current capital of the participants was 2.05 times higher than the non participants. With inequality level

of 0.47, there was high income inequality (0.48) in MSE non participants than their counterparts (0.35). Participation has

a positive impact on poverty which significantly affects on the level of consumption, income and capital of the

households. Number of male adult household members (-0.67), experience of shocks (1.76), sex of the household head

(2.19) and family size (0.38) were the determinants of participation on MSEs, at less than 5 percent level of significance.

Financial problem (28.6 %), poor management practices(14.9%), poor saving habit(14.9 %), conflict among

members(13.1 %), lack of demand driven training(11.9%), market and promotion problem(9.5 %) and others(

administrative) problems( 8.9 %) were the dominant factors for the failure which demands market based short term

trainings focusing on saving, conflict resolution and improving access for finance.

Keywords: Determinant, Gini index, MSE, Poverty and PSM.

1.1. Background and Justification Poverty is a multidimensional concept which addresses different issues, defined differently by different scholars,

and mainly rests on the situation of deprivation experienced by human beings. Constance F. et al., (1995) cited in Araya

M. (2010), define poverty as economic deprivation. A way of expressing this concept is that it pertains to people's lack of

economic resources (e.g., money or near-money income) for consumption of economic goods and services like food,

housing, clothing, education and transportation. The World Bank (2007) defines poverty as "the inability to attain a

minimum standard of living.” Townsend (1979) cited in Esubalew(2006) defines poverty when individuals, families or

groups in a society lack adequate resources to satisfy their wants and needs, or else to participate in the activities and

have the living conditions and amenities, which are common to the society. Furthermore, Lipton and Ravallion (1993)

defines it as a situation existing when one or more persons fall short of a level of economic welfare believed to comprise

a reasonable minimum, either in absolute sense or by the standards of a specific society.

Women are regarded as the world’s poor because the majority of the 1.5 billion people living on 1 dollar a day or

less are women and earn an average slightly more than 50 percent of what men earn. The core source of the entire gender

differential in poverty is that women relative to men are more vulnerable because of the socio-cultural framework of

human society, less educated in the population, cultural values, and ethnicity and lack of physical and human capital. The

socio-cultural beliefs are the limiting factors, which limits the opportunities and capabilities of women, and make them

resource less and powerless individuals (Fitsum T., 2002, Mok T.Y, et al, 2007).

The micro and small enterprises contribute to the reduction of poverty and vulnerability of poor through enabling

them to break the vicious cycle of poverty and also enabling them to enhance self empowerment, respect and social

dignity. It allows poor people to increase their income, accumulate assets, and enter into mainstream society. The benefits

of starting micro-enterprises go beyond an individual and a household. Others in the society are also get benefited from

the microenterprise development as it fosters social relations or networks, civic engagement, community solidarity, and

social capital (Ssewamala et al., 2006).

Micro and small enterprises are also play key roles in a society including contributing to jobs through innovations

and creativity as well as aiding human resources development. The immediate and the long run effect is that they affect

levels of income and ultimately contributing to poverty alleviation (Daniel A., 2010). Now a days, the role of Micro and

Small-scale Enterprises (MSEs) in socio-economic development as a means for generating sustainable employment and

income is increasingly recognized. In developing countries, the informal sector is a large source of employment and

income, particularly for the urban population. The informal employment, outside of agriculture, is defined as

employment that comprises of both self-employment, in the informal enterprises, and wage employment, in the informal

jobs, without secure contracts, worker benefits, or social protection and represents nearly half or more of the total non-

mailto:[email protected]


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agricultural employment in all regions of the developing world. It ranges from 48% in North Africa to 51% in Latin

America, 65% in Asia, and 72% in sub-Saharan Africa (ILO, 2002).

Ethiopia, by now, is recognized as one of the least developed countries showing continuous economic growth of double

digit for a decade, able to attract foreign direct investment and multinational organization to work with. To achieve this

economic growth, the country has practiced different intervention and has developed and implemented different poverty

reduction, food security and sustainable development policy and strategies for five years term since 2001 termed as

Poverty Reduction and Sustainable Development Program (PRSDP)of the first five year plan(2000/1-2004/5), in the 2nd

five years plan(2004/05-2010/11) called Plan for Accelerated and Sustainable Development to End Poverty (PASDEP)

and currently in the 3rd five years plan which is called Growth and Transformation Plan (GTP) covering the years from

2010/11 to 2014/15. In all these plans and programs, the role of micro and small enterprises on poverty reduction and

women empowerment through diversifying their income alternative schemes was credible understood and took

significant policy and government support (MoFED, 2011).

Economic growth brought with its positive trends in reducing poverty, in both urban and rural areas of Ethiopia.

While 45.5 percent of the Ethiopian population was living below the poverty line with extreme poverty in 199506, it was

reduced to 38.7% in 2004-2005; five years later this was 29.6%, which is a decrease of 9.1 percentage points as measured

by the national poverty line, of less than $0.6 per day. Strengthening different poverty reduction efforts and interventions,

using the Growth and Transformation Plan (GTP), the target is to reduce this further to 22.2% by 2014-2015. Despite the

reduction in the level of poverty, extreme poverty is still a common phenomenon of female headed households than the

male counter parts (MoFED, 2012).

Economic growth in the regions of Ethiopia has the same feature as the economic achievement of the nation. It

shows continuous increment throughout the decade averaging more than 10 percent. The encouragement of micro and

small enterprises are carried out in all regions of Ethiopia. Tigrai, the study area, is on the process of change and shows

economic growth continuously for more than a decade. In its strategic plan (2002-2004), the region has achieved average

growth rate of 10.01 percent. Observing and measuring the current performance, the regional government has increased

its efforts for a better accomplishment than the previous plan and achieved 11 percent average growth rate of the

economy in its strategic plan of 2005/05-2009/10.

In Ethiopia, about half of the urban workforce is engaged in the informal sector. Even if the composition of the

female informal workforce varies across regions, the majority of economically active women in developing countries

makes up a significant share of the micro-enterprise population and is considered critically important for poverty

reduction strategies (Gebrehiwot & Wolday, 2005). According to the Ethiopian Central Statistical Authority (2004),

almost 50% of all new jobs created in Ethiopia are attributable to small businesses and enterprises, and roughly 49% of

new businesses that were operational between 1991 and 2003 were owned by women. Moreover, Ethiopian Economic

Association (2004) stated that small businesses and enterprises operated by women entrepreneurs contribute significantly

to the national economy in terms of job creation and the alleviation of poverty.

Studies conducted in most developing countries revealed that Micro and Small Enterprises by virtue of their size,

location, capital investment and their capacity to generate greater employment have proved their powerful propellant

effect for rapid economic growth. The sector is also known as an investment potential in bringing economic transition by

effectively using the skill and talent of the people without requiring high level of training, much capital, and sophisticated

technology in Ethiopia. Self-employment in small-scale businesses presents a constructive option for income generation.

The development of MSEs is becoming a very critical issue for the unemployed people, especially for women. In many

developing countries like Ethiopia, a high percentage of small-scale businesses that cater to local needs are controlled or

owned by women (Wolday, 2004).. Studies on this theme mostly focuses on the employment opportunity, income, and

poverty and challenges focusing on the big or capital cities of the regions. However, to the best of the researchers’

knowledge the real impact of women’s participation in MSEs on poverty alleviation in the zonal towns were not studied

so far. Therefore, a research into women participation in Micro and small Enterprises and its impact on poverty

alleviation could highlight the status of women participation in MSEs and its effect on poverty alleviation and

determinants for their participation; and to connect the research works focusing on mega cities with the zonal cities in

Ethiopia.

1.2. Objectives of the Study 1.2.1. General Objective

The overall objective of the research was to assess the impact of women’s participation in urban micro and small

enterprises on poverty in Tigrai

1.2.2. SPECIFIC OBJECTIVES

To examine the status of poverty on women MSEs participants & non participants

To examine the impact of women’s participation in MSEs on consumption & income

To analyze the factors influencing participation of women on MSEs

To identify the challenges facing urban women MSE operators

2. Methodology of Data Analysis 2.1. Sampling

2.1.1. Sampling Frame

Micro and small Enterprises in urban areas of Central and North Western Zones was purposely selected to be the

study area; because the zones are highly populated areas, has un exploited potential that definitely serves as business


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area for MSEs, particularly MSEs focusing on tourism. Secondly, these zones are the research focus area (theme) of

Aksum University for its immediate support through research and community service outreaches.

2.1.2. Sampling Technique and Sample Size

Multistage sampling technique was employed for selecting the representative women. The first stage was

purposively selection of 3 urban areas from Central and North western zones of Tigray1. The second stage was the use of

systematic random sampling, from the list obtained from the Trade and Industry Office (MSEs core process), to obtain

the required women participants and a total of 168 women MSEs Operators was sampled. In addition, 132 non MSEs

participants who are assumed to have similar economic status with sampled MSEs participants before their participation

was included. Therefore, the total 300 sample was allocated to each urban woreda proportionately on the basis of the

available number of MSEs.

2.2. Data Analysis and Model Specification

The study was conducted using both scientific models and descriptive analysis. Simple dispersion and central

tendency measures was utilized to describe some points in the study. Different software packages like STATA 10, P-

Score, DASP were used for the analysis purpose.

2.2.1. Poverty Analysis

To analyze the impact of participation on MSEs on poverty, it demands first to analyze the incidence of poverty. it

was analyzed using the expenditure approach , commonly known as FGT as it was developed by Foster, Greer, and

Thorbecke (1984) and applied in most poverty studies (Fredu, 2008, Araya M, 2010). In measuring the incidence of

poverty at household level, three measures are influencing once; the Head Count Index (P0) which depicts number of

population who are poor, Poverty Gap Index (P1) which measures the extent to which individuals fall below the poverty

line (the poverty gaps) are far away from the poverty line and Poverty Severity Index (P2) that demonstrates not only the

poverty gap but also the inequality among the poor (WBI, 2005).

Given that Z is the poverty line, Yi is the actual Expenditure (adult equivalent) of individuals below the poverty

line, n is number of people, q is the number of poor people normally those below the poverty threshold, α is poverty

aversion parameter2 , the formula is given as (Fredu, 2008, Tassew etal, 2008, Tesfaye (2006), WBI, 2005 and Araya M,

2010).

Then, the FGT or Pα is given by:

q

i

i

Z

YZ

nYZP

1

1),(

Therefore, if the value of α =0, the FGT or the Pα becomes the Head Count Index (P0) yet when α has value 1, Pα is the

Poverty Gap Index (P1).

2.2.2. Income Inequality

MSEs are expected to be source of income for the household and enable individuals to escape out of poverty. Thus,

studying the impact of these on poverty has to address the issue of income and its distribution. To deal with, income

inequality was analyzed using the popular measure of inequality, Gini coefficient (GC).

In most empirical findings focusing on income inequality, gini index is the dominant one and is represented by the

following formula in which lest assume Xi be a point on the cumulative percentage of population that lies on the

horizontal or (X-axis) and Yi is a point of cumulative percentage of expenditure plotted on the vertical or Y-axis, then

the Gini coefficient(GC) is given by the formula(WBI(2005), Tesfaye(2006) , Tassew et al(2008) and Araya M., 2010).

1

1

11)(

ii

N

i

ii YYXXGCGini

Where Xi is value on the cumulative percentage of population

Yi is value of cumulative percentage of expenditure

N is sample size

2.2.3. Impact Analysis

Assessing the impact of any intervention requires making an inference about the outcomes that would have been

observed for program participants had they not participated. The appropriate evaluation of the impact of the program

requires identifying the average treatment effect on the treated (ATT) defined as the difference in the outcome variables

between the treated households and their counterfactual. Counterfactual refers to what would have happened to the

outcome of program participants had they not participated (Rosenbaum and Rubin, 1983; Gilligan et al., 2008 cited in

Yibrah H., 2010).

Access to the MSES was not randomized. As a result, the impact of the Micro and Small Scale Enterprises on

women households’ poverty reduction and income was estimated using propensity score matching as a method of

estimating the counterfactual outcome for MSES beneficiary households. Estimating the propensity score and making

that the balancing condition satisfied is the first step in propensity score matching (PSM) based on observed household

1 Shire, Aksum and Adwa were selected purposefully as these are the major urban areas in the zones having well functioning MSEs

2 α is value given by researchers(0, 1, or 2) to determine the degree to which the measure is sensitive to the degree of deprivation for theses below the poverty line and higher values of α shows greater weight is placed on the poorest section of the society.


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characteristics. The magnitude of a propensity score ranges between 1 and 0; the larger the propensity score, the more

likely the household is to participate in the Program (Setboonsarnget al., 2008). Variables used in propensity score

estimation in this study will be age of the household head, sex of the household head, education status of the household

head, male adult equivalent, female adult equivalent, pre-program consumption, pre-program income, and family size,

access to credit service, access to health service, and access to communication.

According to Rosenbaum and Rubin (1983), let YMSE

be the outcome of the MSES beneficiary households and

YMSEnon outcome of the Non-MSEs beneficiary households. For each household, only Y

MSE or

YMSEnon is observed, which leads to a missing data problem. In estimating the propensity score, the dependent

variable used was participation in the MSES and Let Di denotes the participation indicator equalling 1 with probability of

if the household is MSES beneficiary households and 0 with probability of 1- otherwise. Let Xi denotes a vector of

observed individual characteristics used as conditioning variables.

The main goal is to identify, the most common evaluation parameter of interest, the average effect of the treatment on the

treated (ATT). It is defined as:

)1......()1()1(),1(

DEDEXDEATT YYYYMSEnonMSEMSEnonMSE

This parameter estimates the average impact among MSES beneficiary households. The data on MSES beneficiary

households identify the mean outcome in the treated state ),1( XDE i

MSE

Y . The mean outcome in the non-MSES

),1( XDE i

MSEnon

Y

is not observed.

Let P= )1(Pr XD denote the probability of participating in the program (MSEs), i.e., the Propensity Score.

Propensity Score Matching (PSM) constructs a statistical comparison group (Non-MSEs beneficiary households) by

matching observations on MSEs beneficiary households to non-MSEs beneficiary households on similar values of

propensity score. The effectiveness of PSM estimators as a feasible estimator for impact evaluation depends heavily on

the assumptions of Conditional Independence Assumption (CIA) and common support assumption (CSA) (Rosenbaum

and Rubin, 1983).

Building on these underlying assumptions, Propensity Score Matching provides a valid method for estimating

),1( XDE i

MSEnon

Y and obtaining unbiased estimates of ATT (Heckman et al., (1997), Smith and Todd (2001),

Smith and Todd (2005)).

Following the parameter of interest here is the average treatment effect on the treated (ATT). Therefore, applying the

composite assumption, the ATT can be written as follow:

)2))......((,1()( XPDEATT YYMSEnonMSE

XPPSM

)3)}...()(,1())(,1({)( XPDEXPDEEATT YYMSEnonMSE

XPPSM

The perception is that two individual households with the same probability of participation will show up in the

participants and non-participants samples in equal proportions on the basis of propensity scores.

Where the first term on the right hand side of the above expression (Equation 03) can be estimated from the MSES

beneficiary households and the second term from the mean outcomes of the matched (i.e. based on propensity score) non-

MSEs beneficiary households.

The probability of participation in the MSES can be derived by binary response models. Following Todd (1995) cited in

Liebenehmet et.al (2009), who finds that various methods to predict propensity score produce similar estimates, for

computational simplicity in this study logit model was applied. The propensity score can then be defined as:

Where F

(Xβ) produces response probabilities strictly between zero and one

Once the propensity score is estimated, the data is split into equally spaced intervals of the propensity score. This

implies that, within each of these intervals, the mean propensity score of each covariates do not differ between the treated

(MSEs beneficiary) and control group (non-MSEs beneficiary) households. This is known as the balancing property.

3. Findings and Results 3.1. Basic Features of Respondents

300 samples were distributed to three urban areas of the Central and Northwestern zones. From the total sample

size, Adwa took 34.69 %, Aksum (39%) and the remaining (26.33%) belongs to shire. From the total sample size, 56

percent of them were MSE operators and the remaining 44 percent were non MSE members.

Marital status profile of the respondents has comprised of all types of status. 44.33 percent of the respondents were

married followed by unmarried 42.33 percent; divorced 10 percent and 3.33 percent of them were also widowed. 46.33

percent of the target respondents were high school complete (9-12), diploma and above (22.33%), secondary (7-8) has a

share of 15.33 percent followed by illiterate (8.67%) and elementary (7.33%). The mean family size of the respondents

was 2.07 with maximum number of household members of five and the m minimum was one. Accordingly about 48.67

percent of the respondents were singled member households followed by two members (17.67%), three members

(16.67%), four (11.33%) and five (5.67%).

)4....(..../1Pr .

11

X

ii eXFxxFXDXP


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The age category of the target respondents was in the age range of 16-70 years. The mean age of the respondents is

30.84 years with standard deviation of age 9.97 years. Based upon the number of MSEs established in the urban areas of

the Central and North western zones of Tigrai, the sample size, for both the participants and non participants, was

allocated proportionately. From the total sample size (300), 56 percent was the share of the MSE and the remaining 44

percent was covered by the non-MSE participants. Accordingly, 39 percent of the sample was allocated to Axum,

followed by Adwa (34.67 %) and Shire (26.33 %).

3.2. Poverty Situation of Respondents

Poverty reduction is still an ultimate objective of the government of Ethiopia which is stated in its Growth and

Transformation Plan of the country. In urban areas of the country, poverty reduction efforts are expressed by the

activities and focuses given to the establishment of MSEs. It is through this and allied interventions; the urban youth and

unemployed people are expected to generate income that enables them to escape from poverty. The poverty situation of

the study area was analyzed with the help of FGT method using the poverty line used to study poverty situation in

Aksum3.

The incidence of poverty in the study area as measured by the Head count index (P0), Poverty gap index (P1) and

poverty severity index (P2) is stated below. Using the food poverty line, table 1, the poverty head count index in the non-

MSE participants (0.34) was higher than the participants (0.245). However, the poverty gap index in the MSE

participants (0.078) was greater than their counterpart living 4.2 percent far away from the poverty line. In addition, the

poverty severity index was also higher in MSE participants (0.035) than the non- participants (0.008). This shows that the

non-MSE participants were living around the poverty line and with less inequality among these who were living below

the poverty line than the participants.

Table 1: Incidence of poverty on the basis of participation

Group P0 P1 P2 Poverty line

Non-MSE 0.340(0.038) 0.042( 0.007) 0.008 (0.002) 231.6

*0.272(0.036) 0.056 ( 0.009) 0.021(0.005) 336

MSE 0.245(0.040) 0.078 ( 0.011) 0.035(0.009) 231.6

*0.201(0.038) 0.021( 0.005) 0.003( 0.001) 336

Population 0.299(0.028) 0.062(0.007) 0.023(0.005) 231.6

*0.242( 0.026) 0.041 ( 0.006) 0.013( 0.003) 336

Source: Researchers’ survey and computation, August 2012

Value in brackets is standard deviations

*incidence of poverty using total poverty line

Poverty can also be measured by the total poverty line, i.e, taking into account the food and non good expenditures

of the households. In this study, as stated above, the total poverty line is birr 336 in which 31.07 percent (birr 104.4)

accounts for non food poverty line. As depicted in table 1, the incidence of poverty among the participants and non MSE

participants is different. 24.5 percent of the non-MSE participants were living below the poverty line with income short

fall of 0.056 and poverty severity index of 0.021. on the other way round, 20.1 percent of the MSE participants were

living below birr 336 and were living 2.1 percent far away from the poverty line and severity index rate of 0.003. Thus,

the poverty gap index and poverty severity index of the non-MSE participants were relatively higher than the MSE

participants if we able to compare using the total poverty line. Most studies carried out with the relationship between

participation in MSEs and poverty revealed that it has the capacity to reduce the extent of poverty. The figures on

incidence of poverty between the participants and non participants revealed that the MSE participants have lower level of

poverty than the non participants. This is quite in line with most numerical finding carried out in this theme.

The incidence of poverty was different in all the study woredas. Accordingly, table 2, 37.2 percent of the respondents

from Shire was living below the poverty line followed by 22.4 percent in Axum and Adwa with head count index of

0.166. The poverty gap index, percentage distance from the poverty line, was also highest in Shire (0.120), followed by

Axum (0.040) and Adwa (0.021). Moreover, the poverty severity index was also led by Shire, Axum and Adwa with

respective magnitudes of 0.064, 0.015 and 0.003, respectively.

Table 2: Incidence of poverty at woreda level

Group P0 P1 P2 Poverty line=336

Shire 0.372(0.072) 0.120(0.034) 0.064(0.021)

Axum 0.224 (0.055) 0.040 (0.016) 0.015 (0.011)

Adwa 0.166(0.047) 0.021(0.006) 0.003( 0.001)

Population 0.242(0.033) 0.041 (0.011) 0.013(0.007)


Value in brackets is standard deviations

3.3. Arrangement of Micro and Small Scale Enterprises (MSEs)

3.3.1. Types of MSEs

Respondents have been involved in different sectors of the economy so as to support and sustain their life through

their direct involvement on income generating schemes. The MSE participants were involved in seventeen different

3 This poverty line is considered as a proxy measure to study poverty in the neighbor study areas with total poverty line of birr 336 and food poverty line of birr 231.6

developed by Araya M., et al(2010) to study determinants of poverty and income inequality in Aksum town


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categories of sectors. 15.33 percent of the respondents were involved in the retail shops, beauty salon(10 %), Baltina(sale

of food related consumables) accounts 9.33 percent, restaurant(9%), wood & metal works(9%), coffee house(8%) were

among the leading sectors.

According to the study made, from the total MSE participants, 11.1 percent of the operators were established before

and in 2005, 20.9 percent of them were established in 2008, 30.36 percent were started operation in 2009 and the

remaining 36.9 percent were also founded in 2010. The number of MSEs established had increased by average yearly

growth rate of 47.4 and in 2008 highest establishment of MSEs (75 %) was recorded.


Having 4.46 mean numbers of members, the maximum and minimum number of members in the MSEs was 25 and

1, respectively. The average age of MSE participants was 30.0 years and the non- MSE participants had mean age of

29.36 years. Moreover, the mean family size of the MSE participants was 2.08 and the non-participants had mean family

size of 1.95. Further, there was no such significant difference between the mean hours that MSE participants (12.12) and

non-participants (12.137) spent on work per day.

3.3.2. Capital and Income Structure

Initial investment is a key element in the study of impact of participation on MSEs. Off course, success or failure of

businesses might depend on so many factors but having huge startup capital is helpful to have well organized kind of

business. To this end, studies shows that initial capital of MSEs can affect their success stories positively.

Capital in our study refers to the money and the current monetary worth of both fixed and variable assets owned by the

respondents. Based on this, the mean initial capital of the MSE operator respondents’ was 5486.31 with standard

deviation of birr 9847.42. On the other way round, the non MSE operators respondents had mean initial capital of birr

5453.182 and standard deviation of birr 3751.09.

Table 3: capital structure of respondents

Type Non-MSE participants MSE participants Total

Mean

Max Min SD Mean Max Min SD Mean Max Min SD

Initial capital

5453.2 18000 1000 3751.1 5486.3 600 500 9847.4 5471.7 60000 500 7767.0

Current capital

5453.2 18000 1000 3751.1 5486.3 600 500 9847.4 5471.7 70000 500 7767.0


The mean current capital of the MSE participants was birr 17021.13 with maximum and minimum capital value of

birr 70000 and 600, respectively and the non participants owned current mean capital of birr 8310.076 and standard

deviation value of birr 4396.379 with maximum and minimum capital holding of birr 2400 and 1000, respectively. Thus,

despite the initial capital of the respondents’ looks very similar, the current capital of the MSE participants was 2.05

times that of the non-MSE participants. This depicts that the MSE participation has the power to increase the capital

holding of the members and this is quite similar with most empirical findings focusing on capital accumulation and MSE

participation.

Ensuring financial security of members and reducing the extreme poverty is the ultimate objective of establishing

and involving in MSE. According to the study made, there was a change in the income level of the MSE participants and

non participants. Income of the MSE operators has increased from time to time. Taking 2008 as a base year of the study,

the mean income of the respondents was birr 670.08. Enjoying an average mean income of birr 3084.697 per year, the

income of the MSE participants reached birr 4393.64 in 2012.

At an average, the mean income of such respondents had increased by 66.13 percent. Despite, ups and downs was

observed in the growth rate of income, the highest mean income growth rate (145.3 %) of the MSE operators had

achieved in 2009 and the lowest growth rate (20.4 %) was recorded in 2011.


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The non MSE participants were able to generate monthly income of birr 623.257 in 2008 and reached birr 2029.05

in 2012 with average mean income increment of birr 1432.13 per year.

The growth rate of mean income of the non MSE participants had shared the same trend of change like that of the

participants. Highest growth rate (66.9%) was recorded in 2009 and the lowest rate of mean income growth (8.8%) was

observed in 2011. Moreover, there is statistically significant difference in the growth rate of mean income of the MSE

operators and non operators, i.e, the participants were enjoying highest growth rate of mean income. Comparing the

current income (2012) of the MSE and non participants, the participant households were able to generate monthly income

level of 2.165 times that of their counterpart.

Table 4: Income inequality before and after participation

Income Inequality Type Estimated STD LB UB

Before

Participants 0.47 0.04 0.43 0.51

Non participants 0.46 0.03 0.44 0.48

Total 0.50 0.02 0.47 0.53

After

Participants 0.35 0.03 0.28 0.42

Non participants 0.48 0.02 0.46 0.49

Total 0.47 0.02 0.44 0.50


The inequality of income before participation, measured in gini coefficient, was 0.50 in which 0.46 was the

inequality in the non- participants and 0.47 in the participants. There was no significant difference between the gini

indices of the participants and non participants.

The current income inequality was reduced to 0.47 and significant decrement has been observed in the MSE

participants (0.35) and slight increment was recorded in the non-participants (0.48). This shows that participation in

MSEs has significant effect to reduce the income inequality as it creates opportunity for households to involve in income

generating activity. Thus, the finding is aligned with most empirical works focusing on the theme of participation in

MSEs and income inequality.

3.4. Impact Analysis

The impact indicator, on poverty4, used in this study for MSEs was consumption, the food and non-food

consumption which was estimated separately and total consumption (food and non-food) in tandem. Consumption was

measured as per adult equivalent, which is food consumption per-adult equivalent, non-food consumption and total

consumption expenditure (food and non-food) per-adult equivalent. The food expenditure includes food items that the

households consumed/used either from their own produced and/ or purchased. The non-food consumption consists of

expenditures on education, medical services, water, electricity and telephone bills, fuel, and personal cares.

The total consumption is the sum total of food and non-food expenditures. A positive value of this indicates that

households receiving or participating in the program called MSEs have higher consumption levels.

Total 5: Consumption expenditure per adult equivalent after matching

Per adult equivalent Obs Mean Std. Dev Min Max

MSE participants 126 421 2.021 256 852

MSE non-participants 87 362 3.265 158 554

Source: Researchers survey and computation, August 2012

To carry out the impact evaluation, from the treated and the control groups we selected, 126 MSE participants and

87 non-MSE participants were left. 42 respondents from the MSE participants and 45 from the non participants were

excluded by the model.

3.4.1. Determinants of Participation

People have different motives to join micro and small enterprises. Taking whether MSE participation or not as

dependent variable (1 if MSE participant and 0 if not), thirteen variables were regressed. As can be seen from table 6,

four variables, male adult household members, shocks, family size and sex of the household head were statistically

significant variables influencing, to join the operation of MSEs as a tool of generating income and escaping out of

poverty, at less than 10 percent level of significance.

4 The poverty was explained by the total household consumption expenditure measured in per adult equivalent


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Table 6: Logit Model Estimates for participation in MSE

Dependent variable: whether a household participated in MSE or not

Variables Coefficients Std. Err t-values

Sex of the household head 2.1900300 0.6301631 3.48

Age of the household head 0.0331605 0.0253169 1.31

Land holding (in hectare) 0.4538186 0.2788640 1.63

Family size 0.3808790 0.1806844 2.11

Access to credit 1.0000400 0.6663582 1.50

Shocks 1.7615820 0.3482018 5.06

Oxen holding (tlu) 0.1021113 0.4487010 0.23

House ownership 0.0251001 00.312013 0.12

Livestock owned (tlu) -0.0380292 0.1495155 -0.25

Educational status -0.1886385 0.5222046 -0.36

Male adult household members -0.6735947 0.2383953 -2.83

Female adult household members 0.4145770 0.2973316 1.39

Intercept -6.2403350 1.7450510 -3.58

Number of observations = 168

Summary statistics

LR chi2(13) = 63.89

Prob > chi2 = 0.0001

Pseudo R2 = 0.21254

Log likelihood = -99.23156


Significant at 1 percent level, Significant at 5 percent level and significant at 10 percent significance level

Sex of the household head, shocks and family size were determining participation positively and having male adult

household member was affecting participation negatively.

The probability of the household to participate in MSE is affected positively by sex of the household head by the

odds ratio of 2.19 at one percent level of significance. Family size of the households was determining participation

positively by the odds ratio of 0.38, at 10 percent level of significance, and experience of shocks at household level was

influencing participation positively by odds ratio level of 1.76. Moreover, having male member in the household was also

determining participation negatively by the odds ratio level of 0.67. This might be due to the cultural fact that male

members need to shoulder responsibility of households and they need not to let females to participate outdoor activities.

Thus, all the significant variables determining participation were supporting the existing empirical findings focusing on

the study of determinants of participation in MSEs.

3.5. Analysis of Impact on Consumption

To examine the impact of the program on households’ consumption expenditure, the sample respondents (both the

MSEs Participants and non-MSEs Participants) were asked for food consumption expenditure either from owned

produced and/or purchased valued in Ethiopian Birr and non-food consumption expenditure. All the quantities derived

were converted into values (Ethiopian Birr) using the mean price of the items in the study year.

The per adult equivalent households’ consumption expenditure is defined as per capita households’ consumption

expenditure adjusted for age and gender of household members obtained by dividing the households’ consumption

expenditure to the adult equivalent family size.

The following two major groups were included in the households’ consumption expenditure: food consumption

expenditures such as cereals (wheat, barley, sorghum, teff, finger millet, etc.), pulses (peas, beans, etc.), vegetables

(onion, potato, garlic, tomato, etc.) cooking oil, meat and other food consumable items, and non-food consumption

expenditures such as educational expenses, expenses on clothing, medical expenses, cleaning and personal care items,

fuel and related expenses. Note that in this study the researchers used the consumption expenditure per adult equivalent

as proxy variable for poverty to evaluate the impact of MSEs on poverty reduction.

3.5.1. MSEs Impact on Consumption Expenditure

The impact of the MSEs on households’ consumption expenditure was measured in per adult equivalent. To

examine the impact of MSEs on household consumption expenditure the researchers used the household survey data of

six months households’ consumption expenditure. In evaluating the impact of the program on the households’

consumption expenditure, the consumption expenditure was computed as per adult equivalent household consumption

expenditure. A positive value of this indicates that households receiving benefits from the program (MSEs) have higher

levels of consumption expenditure per adult equivalent than those who did not benefit from the program, the MSE

participant households; as a result their poverty level is low in relation to the non-participants. Adult equivalent scale has

been used to determine adult equivalent family size (Yibrah H., 2010 and Araya M., 2010).

Table 7 shows that the ATT estimation results of the four matching estimators used for this study. The outcome

indicators for consumption expenditure were evaluated based on semi-annual per-adult equivalent household food

consumption expenditure. The result of this study revealed that on average, the MSEs participant households consumed

more food items in terms of food value as compared to the MSEs non-participant households, which means that the


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poverty level of the MSEs participants in terms of food consumption expenditure was much more better than that of the

MSEs non-participants participants.

Table 7: ATT Estimation Results of Impact of MSE on Households’ Consumption Expenditure per Adult Equivalent

(Poverty)

Outcome variable/

consumption

Expenditure

Matching

method

No of MSE-

Participants

No of MSE non-

participants

Average treatment

effect on the

treated (ATT)

t-values

Total food Nearest

neighbor

101 66 654.421 3.2012

Stratification 101 66 774.524 4.2310

Kernel 101 66 561.251 3.6245

Radius 101 66 780.113 5.2216

Total Non-food Nearest

neighbor

101 66 28.4598 0.2458

Stratification 101 66 45.8562 1.2560

Kernel 101 66 66.4859 0.2356

Radius 101 66 71.0568 1.0026

Total consumption

Expenditure

Nearest

neighbor

101 66 250.554 2.6532

Stratification 108 82 362.1542 3.2154

Kernel 108 82 202.8547 3.0214

Radius 108 82 198.6541 3.2540

Source: Own Computed from Household Survey Data, August 2012

Significant at 1 percent level of Significance; standard errors are bootstrapped.

The difference in the mean value of food consumption per adult equivalent between the MSEs participant and

MSEs non-participant households was found to be positive and significant. Statistically, this was found to be significant

at less than 1 percent significance level based on the NNM (ATT=654.42, t= 3.20), stratification (ATT= 774.524, t=

4.23), kernel (561.25, t=3.62), and radius (ATT= 780.11, t=5.22) matching estimators with bootstrapped standard error.

Therefore, the overwhelming majority of the MSE participating households’ in the program consumed more food items

valued in Birr, which indicates that the poverty level of the MSEs participant households’ are much more better than that

of the MSE non-participant households.

3.5.2. MSEs Impact on Total Consumption Expenditure

Here, the researchers are much more interested on total consumption (food and non-food) expenditure to be used as

a proxy variable to evaluate the impact of MSEs on poverty by means of total expenditure per adult equivalent. In this

estimation, the total food and non-food consumption items of each respondent households was used to obtain the total per

adult equivalent consumption expenditure. Thus, the result indicates that the total per adult equivalent consumption

expenditure for the MSEs participant households was found to be higher as compared to that of the MSEs non-participant

households, which means that the poverty level of the MSEs participant households is much more better than that of the

MSEs non-participant households.

The level of mean consumption expenditure per adult equivalent was birr 362 and 421 for the non MSEs and MSEs

participant households, respectively. The estimated results of the PSM techniques indicated that the mean total

consumption per adult equivalent of the MSEs participant households was significantly higher than that of the MSEs

non-participant households. Statistically, this was found to be significant at less than 1 percent level of significance based

on the NNM (ATT= 250.55, t=2.65), stratification (ATT=362.15, t= 3.21), kernel (ATT= 202.85, t= 3.02), and radius

(ATT= 198.65, t= 3.25) matching estimators with bootstrapped standard error.

3.6. Challenges in Micro and Small Enterprises

The well functioning of MSEs was not free of any challenges. Studies carried out with this respect have proved that

their normal operation is influenced by financial and non financial difficulties.

According to the study made, MSE operators were exposed to different problems which emanates from the internal

activities of the businesses and external situations have also impeded their activities.

Chart 2: Challenges in MSEs



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As depicted in chart 2, financial problem (26.8%) was one of the most impediments facing MSE operators not to

run as the required and enable them to bring the desired result. They faced shortage of finance to establish their business,

to expand their work and to go with the changing environment. Especially, the intension of the government is to support

the cooperative types of business and little attention was given to privately owned MSEs for the financial matters.

The second influencing factors were poor management practices (14.9 %) and poor saving habit (14.9%) of the

business owners. The business managers were lacking special talent to run the business, mostly this was observed in the

cooperative and partner kinds of MSEs. In addition, this was also a challenge in the privately owned businesses though

the degree was low. Further, low saving habit was a common problem to 14.9 percent of the MSE operators.

Among the partner and cooperative types of MSEs, conflict among members and losing the mutual thrust habit was

observed. Around 13.1 percent of the MSE operators were challenged their smooth day-to- day operation by the conflict

created among and between members. This was expressed by missing the normal assigned tasks, become less responsible

and sometimes thefts and embezzlements were created.

The government and other stakeholders having a say on the MSEs are expected to fill the skill and demands of the

operators. Mostly, they are responsible to provide training, consultancy and financial demands of the members. However,

the training given to MSE operators was not as the need of the members. As such the important and most demanded

kinds of traing were not given to customers. 11.9 percent of the operators were not satisfied by the trainings and

consultancy services given by the government and stakeholders due to the fact that despite the training is given in a

redundancy manner it lacks to relate to the current demands of the MSE operators and there was also partial treatment

focusing on the partner and cooperative types of MSEs.

9.5 percent of the MSE operators were facing marketing problem for their products; they had poor market

connection for their products. As such, they could not able to get the required income for their consumption and to

expand their market and the remaining 8.9 percent (others), problems related to the weak treatment of the local

administrators, office of trade and industry-MSE cluster and poor interest of operators after they establish the MSE were

influencing the normal operation of the businesses.

4. Conclusions and Recommendation 4.1. Conclusions

The government of Ethiopia has set an urban poverty reduction policy and strategy to reduce if possible to eradicate

the extreme poverty in urban areas and empower women through the establishment of MSEs. To study the impact, 300

female respondents were selected from three urban areas of the Central and North Western zones (Adwa, Axum and

Shire). Different software packages like STATA V-10, DASP, P-score were used to ensure the quality of the analysis.

The overall incidence of poverty was 0.242 in which the magnitude was less in the MSE participants (0.201) than the

MSE non participants (0.272). Poverty was highest in Shire (0.372), followed by Aksum (0.224) and Adwa (0.196).

With mean expenditure of birr 362 and birr 421, for MSE non participants and participants, respectively; there was

significance difference among the respondents at all measures of impact(Nearest neighbor, Stratification, Kernel and

Radius) at less than one percent level of significance which depicted participation of women on MSEs has a positive

impact on poverty reduction.

Despite similarities have been observed in the mean initial capital birr worth of birr 5471.7, current capital of the

MSE operators (birr 17021) was higher than the MSE non participants (birr 8310.1). There was a change in the income

level of the MSE participants and non participants. Income of the MSE operators has increased from time to time.

Taking 2008 as a base year of the study, the mean income of the respondents was birr 670.08. Enjoying an average mean

income of birr 3084.697 per year, the income of the MSE participants reached birr 4393.64 in 2012. There were ups and

downs on the growth rate of income; highest mean income growth rate (145.3 %) of the MSE operators had achieved in

2009 and the lowest growth rate (20.4 %) was recorded in 2011. There is statistically significant difference in the growth

rate of mean income of the MSE operators and non operators, i.e, the participants were enjoying highest growth rate of

mean income. Current income of the participants was 2.165 times higher than the non participants ensured the positive

impact of participation on MSEs on income. Having income inequality level of 0.47, high income inequality was

observed in the MSE non participants (0.48) than the MSE operators (0.35) which depicted participation has positive

impact to bring fair distribution of income in the households.

People have different motive to participate in MSEs. Number of male adult household members(-0.67) , experience

of shocks(1.76) , sex of the household head(2.19) and family size(0.38) were determining participation on MSEs at 1

and 5 percent level of significance, respectively.

Internal and external challenges were influencing the normal functioning of MSE operators. Financial problem

(28.6 %), poor management practices(14.9%), poor saving habit(14.9 %), conflict among members(13.1 %), lack of

demand driven training(11.9%), market and promotion problem(9.5 %) and others( partial treatment, administrative

problems …) accounting 8.9 percent of share were the dominant variables.

4.2. Recommendation

As participation on MSEs enables households to improve their income, consumption and capital holding, the

government offices which are responsible to support the MSEs have to work hard to mobilize women to participate in

MSEs, in order to reduce the incidence of poverty and income inequality. To achieve this, different experience sharing

days have to be organized to share the MSE participants to the non-MSE participants on the benefits they enjoy and their

current welfares. Thus, Trade and Industry Offices, Woreda Administration and Social Affairs Office and Women, Youth

and Children Office are much responsible.

There are different forms of ownership of MSEs that women are participating. It is good and timely important to

encourage such diversification for its better performance and risk aversion. As the focuses of the government, to support


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the MSEs, were geared towards the partnership and cooperatives MSEs, little attention was given to the private one.

Since the private constitutes the major share and have significant influence as well as share better performance, the

government offices should provide them the necessary supports, encouragements, impartiality, equal access for all, to

bring the desired change on poverty. For this, the Woreda Administration, Trade and Industry Office and TVET are

expected to discharge this responsibility.

Source of finance is the most challenging thing in the smooth operation of MSEs. Operators were influenced by

finance not to expand their business and to provide what the market demands. The poor access for finance is the result of

the poor financial system arrangement in the nation and the poor saving habit of the people, MSE operators. The financial

difficulty of MSE operators might be solved through establishing a kind of loan given resulting from the saving

contribution that MSE operators made. In addition, the contributions of the informal institution like ‘Equib’ to saving and

improving the financial source of the small business operators is very vital. Thus, introducing such local social

arrangement, with some improvements like legality and security, will enable to solve financial issues of households for a

short period of time. Moreover, providing opportunity and access to finance for the privately owned MSEs like other

forms of MSEs helps to reduce their financial difficulty and improve their competitive power in the market. The office of

Trade and Industry, Dedebit Credit and Saving Institution, Woreda Justice Office, and Woreda Administration are the

responsible offices to cooperatively solve the obstacles.

Enhancing market opportunity and giving timely demanded trainings for the MSEs operators are key tools to

expand their market, improve their technical and managerial skills and enable to generate better income from the sector.

Promotional and market linkage works have to be carried out to increase the awareness of the customers towards the

locally produced goods and services and to widen the market for MSEs. Different demand driven trainings focusing on

conflict resolution, entrepreneurship, business management, work ethics, saving and money management, technical skills

have to be given to all the MSE operators. To this end, Trade and Industry Office, TVET, Women, Youth and Children

office are much responsible to handle in collaboration with other offices.

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